Recent advances in computational design of structural multi-principal element alloys
Why this work is in the frame
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Bibliographic record
Abstract
Multi-principal element alloys (MPEAs) have gained extensive interest for structural applications owing to their excellent strength, fracture toughness, wear resistance, creep resistance, and fatigue resistance. In this review, recent progress in the computational design of MPEAs for structural applications is outlined. This includes the scientific advancements achieved through computational methods in the field of structural MPEAs, how new methodologies have emerged due to the needs of complex alloy systems, and adaptations to the existing tools to address emerging problems in the field. We discuss advances in atomistic simulation methods, including structure generation algorithms, element-resolved local lattice distortion, chemical short-range order, local slip resistance, and radiation tolerance, along with experimental comparisons. A detailed discussion on interatomic potentials is included, with a focus on various machine learning-based fitting methods. The application of data science and machine learning for identifying and discovering MPEAs with desirable mechanical performance is summarized and presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it